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covariance

  1. Nicole Seaman

    P1.T2.20.13. Coskewness and cokurtosis

    Learning objectives: Use sample data to estimate quantiles, including the median. Estimate the mean of two variables and apply the CLT. Estimate the covariance and correlation between two random variables. Explain how coskewness and cokurtosis are related to skewness and kurtosis. Questions...
  2. Nicole Seaman

    P1.T2.20.9. Linear transformation of covariance and correlation

    Learning objectives: Define covariance and explain what it measures. Explain the relationship between the covariance and correlation of two random variables and how these are related to the independence of the two variables. Explain the effects of applying linear transformations on the...
  3. Nicole Seaman

    CFA Level 1 CFA: Correlation, covariance and probability topics

    Session 2, Reading 9 (Part 2): This video reviews portfolio variance and covariance, where covariance is the expected cross-product. We look at correlation, which is given by the covariance divided by the product of standard deviations, and therefore standardizes the covariance into a unitless...
  4. Nicole Seaman

    YouTube T2-8 Covariance: population vs. sample, and relationship to correlation

    Covariance is a measure of linear co-movement between variables. Independence implies zero covariance, but the converse is not necessarily true (because variables can be dependent in a non-linear way). Here is David's XLS: http://trtl.bz/2B9nqdO
  5. Nicole Seaman

    YouTube T2-4 What is statistical independence?

    Variables are independent if and only if (iff) their JOINT probability is equal to the product of their unconditional (aka, marginal) probabilities; i.e., if and only if Prob(X,Y) = Prob(X)*Prob(Y). Further, if variables are independent then their covariance (and correlation) is equal to zero...
  6. V

    R13-P1-T2- Miler Page 35 Question- Calculating Covariance & Correlation

    Can someone explain how mean & variance have been calculated in this example?
  7. Nicole Seaman

    P1.T2.711. Covariance and correlation (Miller, Ch.3)

    Learning objectives: Calculate and interpret the covariance and correlation between two random variables. Calculate the mean and variance of sums of variables. Questions: 711.1. The following probability matrix displays joint probabilities for an inflation outcome, I = {2, 3, or 4}, and an...
  8. kevolution

    Covariance matrix vs variance formula for 2-asset question

    I was looking at this specific 2-asset portfolio example and noticed that BT uses the matrix formula to get the variance of P. What I'm confused about is why do you not use the variance formula: variance = X1^2*stddev(asset1)^2 + X2^2*stddev(asset2)^2 +...
  9. Nicole Seaman

    P1.T2.706. Bivariate normal distribution (Hull)

    Learning objectives: Calculate covariance using the EWMA and GARCH(1,1) models. Apply the consistency condition to covariance. Describe the procedure of generating samples from a bivariate normal distribution. Describe properties of correlations between normally distributed variables when using...
  10. Nicole Seaman

    P1.T2.705. Correlation (Hull)

    Learning objective: Define correlation and covariance and differentiate between correlation and dependence. Questions: 705.1. In order to evaluate the the potential of a linear relationship between portfolio returns and a benchmark index, your colleague Richard conducted a univariate...
  11. Nicole Seaman

    P1.T2.506. Covariance stationary time series

    Learning outcomes: Define covariance stationary, autocovariance function, autocorrelation function, partial autocorrelation function and autoregression. Describe the requirements for a series to be covariance stationary. Explain the implications of working with models that are not covariance...
  12. Nicole Seaman

    P1.T2.502. Covariance updates with EWMA and GARCH(1,1) models

    Learning outcomes: Define correlation and covariance, differentiate between correlation and dependence. Calculate covariance using the EWMA and GARCH (1,1) models. Apply the consistency condition to covariance. Questions: 502.1. About the consistency condition, each of the following is true...
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